MétaCan
Menu
Back to cohort
Record W2542344939 · doi:10.25439/rmt.27581103

The causes and consequences of operational risk: some empirical tests

2016· dissertation· en· W2542344939 on OpenAlexaboutno aff

Bibliographic record

VenueRMIT Research Repository (RMIT University Library) · 2016
Typedissertation
Languageen
FieldEconomics, Econometrics and Finance
TopicBanking stability, regulation, efficiency
Canadian institutionsnot available
Fundersnot available
KeywordsOperational riskEmpirical researchEmpirical evidenceBusinessRisk analysis (engineering)EconomicsActuarial scienceRisk managementFinanceStatistics

Abstract

fetched live from OpenAlex

The thesis provides empirical evidence on the causes and consequences of operational risk. First, by including operational losses endured by firms across all sectors worldwide, we investigate the determinants that potentially explain cross-country differences in operational risk. These determinants are based on country-level information. They can be broadly classified into three categories measuring three unique dimensions of a country: macroeconomic, regulatory, and social. To circumvent model-specification issues and variable-selection bias, we carry out the empirical work according to extreme bounds analysis (EBA), which is an econometric modelling approach suggested by Leamer (1983, 1985) and further extended by Granger and Uhlig (1990), as well as Sala-i-Martin (1997).<br><br>The empirical results show that operational-loss severity, on average, rises as a country’s GDP level and the cost of living increase. In addition, a country as a whole is more likely to experience catastrophic losses with a poorer regulatory and governance standard, particularly against the background of the rigorous process by which a country’s government is selected, monitored, and replaced; and also on the capacity of that government to formulate and implement sound policies effectively. Furthermore, the overall development of a country’s citizens—including their life expectancy, education, and income levels—also plays a role when comparing operational-loss severity from one country to another.<br><br>Second, to address the consequences of operational risk, we use an event-study approach to examine the economic impact of operational-loss announcements on firms’ stock market value and the potential reputational damage that follows. We distinguish operational-loss settlement news from its initial press release to detect potential discrepancies in market reactions to the two announcement types, and we examine the effect of gradual information release. We account for the nominal amount of operational losses to separate the reputational effect of the loss announcement from its direct monetary impact, hence refining the measures of reputational risk. We scope the empirical estimation at a firm-level for 331 operational-loss events settled by commercial banks headquartered in the United States, the United Kingdom, and Canada during the period 1995 to 2008.<br><br>The findings reveal that the stock market reacts negatively to the initial press release of operational-loss events, as well as to its settlement news across all three countries analysed. This negative reaction is more abrupt surrounding the event dates, highlighting the strong initial reaction to loss news, although it fades quickly after the announcements are made to the public. This suggests that the market selloff may be short-lived. Reputational risk is consistently evident in the global and in all of the sub-regional samples, indicating that the market tends to overreact to operational-loss announcement. In addition, the market appears to be more sensitive to announcements of (i) losses resulting from internal fraud; (ii) losses of a bigger magnitude with an undisclosed loss figure; (iii) losses that result in restitutions, and (iv) losses that are consequences of regulators’ investigation.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.270
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.039
GPT teacher head0.279
Teacher spread0.240 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2016
Admission routes1
Has abstractyes

Explore more

Same venueRMIT Research Repository (RMIT University Library)Same topicBanking stability, regulation, efficiencyFrench-language works237,207